Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks

Jose Eduardo Urrea Cabus; İsmail Hakkı Altaş

Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks

Abstract

This paper compares various unsupervised feature extraction techniques and supervised machine learning models for fault detection and classification over a power distributed generation system. The modified IEEE 34 bus test feeder was implemented for the study case simulated through PowerFactory DigSILENT software. Data analysis results from three-phase voltages and currents collected were performed in Python. Simulation results confirm that by applying dimensionality reduction techniques as feature extraction and wavelet family selection adequately, a high identification and classification accuracy can be obtained, excluding the less essential characteristics and preventing the machine learning models from overfitting or underfitting the datasets.

Keywords

Data mining , fault diagnosis , feature extraction , machine learning

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Öğrenmesi Algoritmaları

Bölüm

Araştırma Makalesi

Yazarlar

Jose Eduardo Urrea Cabus Bu kişi benim
Türkiye

İsmail Hakkı Altaş Bu kişi benim
Türkiye

Yayımlanma Tarihi

21 Aralık 2021

Gönderilme Tarihi

7 Temmuz 2021

Kabul Tarihi

5 Aralık 2021

Yayımlandığı Sayı

Yıl 2021 Cilt: 4 Sayı: 2

IZ

https://izlik.org/JA46KR26BE

IEEE

[1]J. E. U. Cabus ve İ. H. Altaş, “Machine Learning Model Applications for Fault Detection and Classification in Distributed Power Networks”, DataSCI, c. 4, sy 2, ss. 11–18, Ara. 2021, [çevrimiçi]. Erişim adresi: https://izlik.org/JA46KR26BE